@inproceedings{3039670267504c92a5180e7e3555d9d9,
title = "Spatially aware supervised nonlinear dimensionality reduction for hyperspectral data",
abstract = "In this paper we study the effect of injecting spatial information of image patches directly in the process of supervised dimensionality reduction. In particular, we adopt an approach derived from the mean map kernel framework to map image patches of variable size into a reproducing kernel Hilbert space. In that space, the orthonormalized partial least squares performs supervised dimensionality reduction to a discriminant subspace. Advantages of the proposed approach are discussed by studying two well known hyperspectral image benchmarks and by comparing it to composite-kernel feature extraction framework.",
keywords = "Dimensionality reduction, Kernels, Spatial information, Supervised feature extraction",
author = "Michele Volpi and Devis Tuia",
year = "2014",
month = jun,
day = "28",
doi = "10.1109/WHISPERS.2014.8077551",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE",
booktitle = "2014 6th Workshop on Hyperspectral Image and Signal Processing",
address = "United States",
note = "6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 ; Conference date: 24-06-2014 Through 27-06-2014",
}